In life, people often express emotions through face expressions. Face expressions are some of the most powerful, natural, and immediate ways for humans to communicate their emotions and intentions. The face can express an emotion sooner than people verbalize or even realize their feelings. For example, different emotions are expressed using various face regions, mainly the mouth, the eyes, and the eyebrows.
Face recognition technology is widely used today. For example, a physical store may utilize face expression recognition technology to identify the expressions (e.g., happiness or disgust) of consumers when they browse products or advertisements to obtain ratings gave by consumers on the products or advertisements.
However, current expression recognizers typically assign category labels to expression states, such as “anger” or “sad,” relying on signal processing and pattern recognition techniques. A major challenge to such approaches is that human expressive behavior is highly variable and depends on a number of factors. These factors may include the context and domain of the expressive behavior. Therefore, categorical representations for expressions and simple pattern recognition schemes may not accurately recognize face expressions.
Thus, a method and a device for recognizing face expressions are desired to improve the accuracy of face expression recognition.